My Projects
Smart heart disease detection
Trained the model from CSV file using different ML algorithms like SVM, KNN, ensemble method. predicts whether a person has a heart condition or not based on age, sex, cp, trestbps, chol, fbs. It is deployed on the Flask server, implemented End-to-End by developing a Front End to consume the ML model. With ensemble method the accuracy of the output is increased by 10%
online food delivery
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created a front-end website using HTML, CSS, JavaScript which contains Menu, cost of each food item and all the details of the restaurant. The platform allows users to browse food items, place orders, and manage their orders seamlessly.
Alzheimer Disease Detection with HPC and EffiecientNetB3
: the advancement of Alzheimer's illness utilizing Convolution Neural Systems (CNN) and EfficientNetB3 architecture, which was applied to pre-processed MRI datasets. The purpose of the project is to use efficient high-performance computing (HPC) to improve the performance of the model, which makes the diagnostic process more efficient and reliable the result of this project is Training time without HPC optimizations: 976.88 seconds.
